B2/C1 - AI Platform Engineer 2

Capgemini; Charlotte (NC); On-site; USD 51,000 - 80,000; Part time

Job summary

Tundra Technical Solutions in Charlotte, NC is seeking an AI Engineer to design, build, and scale enterprise-grade AI/ML solutions with a focus on generative AI deployments on Google Cloud Platform. The role requires hands-on delivery of customer-facing AI solutions and direct Gemini Enterprise experience.

Responsibilities include building pipelines and services in Python, deploying on Vertex AI and BigQuery, and implementing RAG architectures.

Qualifications

• 7+ years of software/AI/ML engineering experience.
• Hands-on customer-facing experience with Gemini Enterprise (or equivalent enterprise GenAI platform).
• Advanced proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn).
• Solid understanding of LLM fundamentals: prompt engineering, embeddings, vector databases, RAG.
• Experience deploying and scaling ML models in production on GCP.

Responsibilities

• Design, develop, and deploy AI/ML solutions and generative AI applications for enterprise customers.
• Architect and implement solutions leveraging Gemini Enterprise.
• Build and productionize ML pipelines, APIs, and services using Python.
• Design and implement RAG architectures, agentic workflows, and LLM-based applications.
• Deploy, monitor, and optimize models and services on GCP (Vertex AI, BigQuery, Cloud Run, GKE, Cloud Functions, Pub/Sub).
• Collaborate with customers/stakeholders to translate business problems into scalable AI solutions.
• Ensure solutions meet enterprise standards for security, scalability, performance, and cost efficiency.
• Partner with data engineering, MLOps, and platform teams to integrate AI capabilities into existing systems.
• Stay current with evolving GCP AI/ML offerings and recommend adoption where relevant.
• Mentor junior engineers and contribute to best practices for AI/ML development.

Skills

Gemini Enterprise

Python

LLM fundamentals

API design

Tools

TensorFlow

PyTorch

scikit-learn

LangChain

LangGraph

Vertex AI

BigQuery

Kubeflow

MLflow

Job description

Charlotte, NC, United States (On-site)

Contract (5 months)

Published 2 days ago

GenAI Platforms

ML Frameworks

bigquery

performance optimization

Python

MLOps tools

We are seeking a AI Engineer to design, build, and scale AI/ML solutions with a focus on enterprise-grade generative AI deployments. This role requires hands-on experience delivering customer-facing AI solutions on Google Cloud Platform, including direct experience with Gemini Enterprise.

Key Responsibilities:

Design, develop, and deploy AI/ML solutions and generative AI applications for enterprise customers

Architect and implement solutions leveraging Gemini Enterprise (Gemini for Google Workspace / Gemini Enterprise AI agents, grounding, and search)

Build and productionize ML pipelines, APIs, and services using Python

Design and implement RAG (Retrieval-Augmented Generation) architectures, agentic workflows, and LLM-based applications

Deploy, monitor, and optimize models and services on GCP (Vertex AI, BigQuery, Cloud Run, GKE, Cloud Functions, Pub/Sub)

Collaborate with customers/stakeholders to gather requirements and translate business problems into scalable AI solutions

Ensure solutions meet enterprise standards for security, scalability, performance, and cost efficiency

Partner with data engineering, MLOps, and platform teams to integrate AI capabilities into existing systems

Stay current with evolving GCP AI/ML offerings (Vertex AI, Gemini models, Agent Builder, Model Garden) and recommend adoption where relevant

Mentor junior engineers and contribute to best practices for AI/ML development

Required Qualifications:

7+ years of experience in software/AI/ML engineering roles

Hands-on customer-facing experience with Gemini Enterprise (or equivalent enterprise GenAI platforms)

Advanced proficiency in Python (ML libraries: TensorFlow, PyTorch, scikit-learn, LangChain/LangGraph, etc.)

Solid understanding of LLM fundamentals: prompt engineering, fine-tuning, embeddings, vector databases, RAG

Experience deploying and scaling ML models in production environments

Strong understanding of API design, microservices, and cloud-native architecture

Excellent communication skills — able to engage directly with enterprise customers and stakeholders

Preferred Qualifications:

GCP Professional certifications (Machine Learning Engineer, Cloud Architect, or Data Engineer)

Experience with MLOps tooling (Vertex AI Pipelines, Kubeflow, MLflow)

Experience with agentic AI frameworks and multi-agent orchestration

Background in enterprise sales engineering, solutions architecture, or pre-sales technical consulting

Familiarity with data governance, security, and compliance in AI/ML systems

The pay range that the employer in good faith reasonably expects to pay for this position is $36.98/hour - $57.79/hour. Our benefits include medical, dental, vision and retirement benefits. Applications will be accepted on an ongoing basis.

Tundra Technical Solutions is among North America’s leading providers of Staffing and Consulting Services. Our success and our clients’ success are built on a foundation of service excellence. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Unincorporated LA County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: client provided property, including hardware (both of which may include data) entrusted to you from theft, loss or damage; return all portable client computer hardware in your possession (including the data contained therein) upon completion of the assignment, and; maintain the confidentiality of client proprietary, confidential, or non-public information. In addition, job duties require access to secure and protected client information technology systems and related data security obligations.